Overview

Dataset statistics

Number of variables15
Number of observations170653
Missing cells0
Missing cells (%)0.0%
Duplicate rows705
Duplicate rows (%)0.4%
Total size in memory19.5 MiB
Average record size in memory120.0 B

Variable types

Numeric13
Categorical2

Alerts

Dataset has 705 (0.4%) duplicate rowsDuplicates
acousticness is highly overall correlated with energy and 3 other fieldsHigh correlation
danceability is highly overall correlated with valenceHigh correlation
energy is highly overall correlated with acousticness and 2 other fieldsHigh correlation
loudness is highly overall correlated with acousticness and 3 other fieldsHigh correlation
popularity is highly overall correlated with acousticness and 2 other fieldsHigh correlation
valence is highly overall correlated with danceabilityHigh correlation
year is highly overall correlated with acousticness and 3 other fieldsHigh correlation
explicit is highly imbalanced (58.2%)Imbalance
instrumentalness has 46580 (27.3%) zerosZeros
voice_pitch has 21600 (12.7%) zerosZeros
popularity has 27892 (16.3%) zerosZeros

Reproduction

Analysis started2024-01-20 10:38:28.078762
Analysis finished2024-01-20 10:40:00.841891
Duration1 minute and 32.76 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

year
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1976.7872
Minimum1921
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-01-20T12:40:01.299686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1921
5-th percentile1933
Q11956
median1977
Q31999
95-th percentile2016
Maximum2020
Range99
Interquartile range (IQR)43

Descriptive statistics

Standard deviation25.917853
Coefficient of variation (CV)0.013111099
Kurtosis-1.0355084
Mean1976.7872
Median Absolute Deviation (MAD)22
Skewness-0.12943464
Sum3.3734467 × 108
Variance671.73508
MonotonicityNot monotonic
2024-01-20T12:40:01.862449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2018 2103
 
1.2%
2020 2030
 
1.2%
2011 2017
 
1.2%
2010 2016
 
1.2%
2014 2005
 
1.2%
2001 2005
 
1.2%
1974 2000
 
1.2%
1979 2000
 
1.2%
1978 2000
 
1.2%
1977 2000
 
1.2%
Other values (90) 150477
88.2%
ValueCountFrequency (%)
1921 150
 
0.1%
1922 71
 
< 0.1%
1923 185
 
0.1%
1924 236
 
0.1%
1925 278
 
0.2%
1926 1378
0.8%
1927 615
 
0.4%
1928 1261
0.7%
1929 952
0.6%
1930 1924
1.1%
ValueCountFrequency (%)
2020 2030
1.2%
2019 1949
1.1%
2018 2103
1.2%
2017 1992
1.2%
2016 1797
1.1%
2015 1974
1.2%
2014 2005
1.2%
2013 1976
1.2%
2012 1945
1.1%
2011 2017
1.2%

duration_ms
Real number (ℝ)

Distinct51755
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230948.31
Minimum5108
Maximum5403500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-01-20T12:40:02.413595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5108
5-th percentile110987
Q1169827
median207467
Q3262400
95-th percentile411551.6
Maximum5403500
Range5398392
Interquartile range (IQR)92573

Descriptive statistics

Standard deviation126118.41
Coefficient of variation (CV)0.54608936
Kurtosis132.92182
Mean230948.31
Median Absolute Deviation (MAD)44467
Skewness7.3137407
Sum3.9412022 × 1010
Variance1.5905855 × 1010
MonotonicityNot monotonic
2024-01-20T12:40:02.985934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192000 56
 
< 0.1%
170000 50
 
< 0.1%
180000 49
 
< 0.1%
186000 49
 
< 0.1%
184000 48
 
< 0.1%
160000 48
 
< 0.1%
168000 46
 
< 0.1%
240000 46
 
< 0.1%
195000 45
 
< 0.1%
175000 44
 
< 0.1%
Other values (51745) 170172
99.7%
ValueCountFrequency (%)
5108 1
< 0.1%
5991 1
< 0.1%
6362 1
< 0.1%
6467 1
< 0.1%
8853 2
< 0.1%
9680 1
< 0.1%
10371 1
< 0.1%
11973 1
< 0.1%
13453 1
< 0.1%
13600 1
< 0.1%
ValueCountFrequency (%)
5403500 1
< 0.1%
4270034 1
< 0.1%
4269407 1
< 0.1%
4120258 2
< 0.1%
3816373 1
< 0.1%
3650800 1
< 0.1%
3569933 1
< 0.1%
3557955 1
< 0.1%
3556867 1
< 0.1%
3551152 1
< 0.1%

acousticness
Real number (ℝ)

HIGH CORRELATION 

Distinct4689
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50211476
Minimum0
Maximum0.996
Zeros20
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-01-20T12:40:03.538079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00147
Q10.102
median0.516
Q30.893
95-th percentile0.992
Maximum0.996
Range0.996
Interquartile range (IQR)0.791

Descriptive statistics

Standard deviation0.37603173
Coefficient of variation (CV)0.74889597
Kurtosis-1.6094281
Mean0.50211476
Median Absolute Deviation (MAD)0.395
Skewness-0.032582418
Sum85687.391
Variance0.14139986
MonotonicityNot monotonic
2024-01-20T12:40:04.100045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995 3117
 
1.8%
0.994 2323
 
1.4%
0.993 1759
 
1.0%
0.992 1513
 
0.9%
0.991 1298
 
0.8%
0.99 1180
 
0.7%
0.996 1058
 
0.6%
0.989 1053
 
0.6%
0.988 927
 
0.5%
0.987 818
 
0.5%
Other values (4679) 155607
91.2%
ValueCountFrequency (%)
0 20
< 0.1%
1 × 10-61
 
< 0.1%
1.01 × 10-63
 
< 0.1%
1.02 × 10-61
 
< 0.1%
1.03 × 10-61
 
< 0.1%
1.05 × 10-62
 
< 0.1%
1.07 × 10-61
 
< 0.1%
1.11 × 10-61
 
< 0.1%
1.15 × 10-61
 
< 0.1%
1.17 × 10-62
 
< 0.1%
ValueCountFrequency (%)
0.996 1058
 
0.6%
0.995 3117
1.8%
0.994 2323
1.4%
0.993 1759
1.0%
0.992 1513
0.9%
0.991 1298
0.8%
0.99 1180
 
0.7%
0.989 1053
 
0.6%
0.988 927
 
0.5%
0.987 818
 
0.5%

danceability
Real number (ℝ)

HIGH CORRELATION 

Distinct1240
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53739553
Minimum0
Maximum0.988
Zeros143
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-01-20T12:40:04.670954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.23
Q10.415
median0.548
Q30.668
95-th percentile0.811
Maximum0.988
Range0.988
Interquartile range (IQR)0.253

Descriptive statistics

Standard deviation0.17613774
Coefficient of variation (CV)0.32776181
Kurtosis-0.44289742
Mean0.53739553
Median Absolute Deviation (MAD)0.126
Skewness-0.22347135
Sum91708.16
Variance0.031024502
MonotonicityNot monotonic
2024-01-20T12:40:05.252038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.565 430
 
0.3%
0.612 408
 
0.2%
0.632 402
 
0.2%
0.61 401
 
0.2%
0.578 399
 
0.2%
0.559 398
 
0.2%
0.545 397
 
0.2%
0.6 395
 
0.2%
0.548 394
 
0.2%
0.556 393
 
0.2%
Other values (1230) 166636
97.6%
ValueCountFrequency (%)
0 143
0.1%
0.0551 1
 
< 0.1%
0.0559 2
 
< 0.1%
0.0569 2
 
< 0.1%
0.0574 1
 
< 0.1%
0.0583 1
 
< 0.1%
0.0587 1
 
< 0.1%
0.0589 1
 
< 0.1%
0.059 1
 
< 0.1%
0.0591 1
 
< 0.1%
ValueCountFrequency (%)
0.988 1
 
< 0.1%
0.986 2
 
< 0.1%
0.985 1
 
< 0.1%
0.983 1
 
< 0.1%
0.98 4
< 0.1%
0.979 3
< 0.1%
0.978 3
< 0.1%
0.977 5
< 0.1%
0.976 1
 
< 0.1%
0.975 6
< 0.1%

liveness
Real number (ℝ)

Distinct1740
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20583866
Minimum0
Maximum1
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-01-20T12:40:05.824317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0599
Q10.0988
median0.136
Q30.261
95-th percentile0.61
Maximum1
Range1
Interquartile range (IQR)0.1622

Descriptive statistics

Standard deviation0.17480466
Coefficient of variation (CV)0.84923146
Kurtosis5.0012722
Mean0.20583866
Median Absolute Deviation (MAD)0.0532
Skewness2.1543815
Sum35126.984
Variance0.03055667
MonotonicityNot monotonic
2024-01-20T12:40:06.397971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111 1849
 
1.1%
0.11 1663
 
1.0%
0.109 1636
 
1.0%
0.108 1624
 
1.0%
0.107 1553
 
0.9%
0.106 1508
 
0.9%
0.112 1500
 
0.9%
0.105 1481
 
0.9%
0.103 1402
 
0.8%
0.114 1379
 
0.8%
Other values (1730) 155058
90.9%
ValueCountFrequency (%)
0 12
< 0.1%
0.00967 1
 
< 0.1%
0.0101 1
 
< 0.1%
0.0103 1
 
< 0.1%
0.0116 1
 
< 0.1%
0.012 1
 
< 0.1%
0.0123 1
 
< 0.1%
0.0134 1
 
< 0.1%
0.0136 3
 
< 0.1%
0.0139 1
 
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.999 1
 
< 0.1%
0.998 2
 
< 0.1%
0.997 5
 
< 0.1%
0.996 3
 
< 0.1%
0.995 10
< 0.1%
0.994 8
< 0.1%
0.993 5
 
< 0.1%
0.992 13
< 0.1%
0.991 16
< 0.1%

loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct25410
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-11.46799
Minimum-60
Maximum3.855
Zeros0
Zeros (%)0.0%
Negative170622
Negative (%)> 99.9%
Memory size1.3 MiB
2024-01-20T12:40:06.940097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile-22.02
Q1-14.615
median-10.58
Q3-7.183
95-th percentile-4.1306
Maximum3.855
Range63.855
Interquartile range (IQR)7.432

Descriptive statistics

Standard deviation5.6979429
Coefficient of variation (CV)-0.49685628
Kurtosis1.8468035
Mean-11.46799
Median Absolute Deviation (MAD)3.648
Skewness-1.0518411
Sum-1957046.9
Variance32.466553
MonotonicityNot monotonic
2024-01-20T12:40:07.516146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.942 27
 
< 0.1%
-7.436 27
 
< 0.1%
-6.664 26
 
< 0.1%
-8.32 26
 
< 0.1%
-11.815 25
 
< 0.1%
-7.566 25
 
< 0.1%
-7.632 25
 
< 0.1%
-8.992 25
 
< 0.1%
-9.298 24
 
< 0.1%
-7.02 24
 
< 0.1%
Other values (25400) 170399
99.9%
ValueCountFrequency (%)
-60 9
< 0.1%
-55 1
 
< 0.1%
-54.837 1
 
< 0.1%
-54.376 1
 
< 0.1%
-52.22 1
 
< 0.1%
-51.123 1
 
< 0.1%
-51.08 1
 
< 0.1%
-50.174 1
 
< 0.1%
-48.587 1
 
< 0.1%
-48.278 2
 
< 0.1%
ValueCountFrequency (%)
3.855 1
< 0.1%
3.744 1
< 0.1%
2.799 1
< 0.1%
1.963 1
< 0.1%
1.83 1
< 0.1%
1.483 1
< 0.1%
1.342 1
< 0.1%
1.275 1
< 0.1%
1.073 1
< 0.1%
1.023 1
< 0.1%

energy
Real number (ℝ)

HIGH CORRELATION 

Distinct2332
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48238884
Minimum0
Maximum1
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-01-20T12:40:08.200595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.076
Q10.255
median0.471
Q30.703
95-th percentile0.924
Maximum1
Range1
Interquartile range (IQR)0.448

Descriptive statistics

Standard deviation0.2676457
Coefficient of variation (CV)0.55483395
Kurtosis-1.1001327
Mean0.48238884
Median Absolute Deviation (MAD)0.224
Skewness0.11203494
Sum82321.102
Variance0.071634223
MonotonicityNot monotonic
2024-01-20T12:40:08.788990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 247
 
0.1%
0.341 244
 
0.1%
0.187 242
 
0.1%
0.255 241
 
0.1%
0.219 239
 
0.1%
0.274 238
 
0.1%
0.185 238
 
0.1%
0.32 237
 
0.1%
0.245 237
 
0.1%
0.306 237
 
0.1%
Other values (2322) 168253
98.6%
ValueCountFrequency (%)
0 9
< 0.1%
1.99 × 10-51
 
< 0.1%
2 × 10-51
 
< 0.1%
2.01 × 10-57
< 0.1%
2.02 × 10-53
 
< 0.1%
2.03 × 10-510
< 0.1%
2.8 × 10-51
 
< 0.1%
3.22 × 10-51
 
< 0.1%
4.28 × 10-51
 
< 0.1%
4.98 × 10-51
 
< 0.1%
ValueCountFrequency (%)
1 21
 
< 0.1%
0.999 28
 
< 0.1%
0.998 38
< 0.1%
0.997 51
< 0.1%
0.996 64
< 0.1%
0.995 81
< 0.1%
0.994 76
< 0.1%
0.993 73
< 0.1%
0.992 59
< 0.1%
0.991 88
0.1%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct5401
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16700958
Minimum0
Maximum1
Zeros46580
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-01-20T12:40:09.410156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.000216
Q30.102
95-th percentile0.905
Maximum1
Range1
Interquartile range (IQR)0.102

Descriptive statistics

Standard deviation0.31347467
Coefficient of variation (CV)1.8769862
Kurtosis0.94219542
Mean0.16700958
Median Absolute Deviation (MAD)0.000216
Skewness1.631114
Sum28500.686
Variance0.098266371
MonotonicityNot monotonic
2024-01-20T12:40:10.058776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46580
 
27.3%
0.916 201
 
0.1%
0.917 197
 
0.1%
0.904 191
 
0.1%
0.922 191
 
0.1%
0.913 188
 
0.1%
0.911 186
 
0.1%
0.899 185
 
0.1%
0.909 185
 
0.1%
0.914 184
 
0.1%
Other values (5391) 122365
71.7%
ValueCountFrequency (%)
0 46580
27.3%
1 × 10-624
 
< 0.1%
1.01 × 10-667
 
< 0.1%
1.02 × 10-684
 
< 0.1%
1.03 × 10-669
 
< 0.1%
1.04 × 10-656
 
< 0.1%
1.05 × 10-658
 
< 0.1%
1.06 × 10-657
 
< 0.1%
1.07 × 10-667
 
< 0.1%
1.08 × 10-655
 
< 0.1%
ValueCountFrequency (%)
1 10
< 0.1%
0.999 12
< 0.1%
0.998 9
< 0.1%
0.997 4
 
< 0.1%
0.996 6
 
< 0.1%
0.995 6
 
< 0.1%
0.994 8
< 0.1%
0.993 16
< 0.1%
0.992 11
< 0.1%
0.991 6
 
< 0.1%

bpm
Real number (ℝ)

Distinct84694
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.86159
Minimum0
Maximum243.507
Zeros143
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-01-20T12:40:10.668080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile74.1516
Q193.421
median114.729
Q3135.537
95-th percentile174.3738
Maximum243.507
Range243.507
Interquartile range (IQR)42.116

Descriptive statistics

Standard deviation30.708533
Coefficient of variation (CV)0.26277696
Kurtosis-0.077953213
Mean116.86159
Median Absolute Deviation (MAD)21.091
Skewness0.44974062
Sum19942781
Variance943.014
MonotonicityNot monotonic
2024-01-20T12:40:11.226315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 143
 
0.1%
120 20
 
< 0.1%
120.012 20
 
< 0.1%
94.997 19
 
< 0.1%
128.005 19
 
< 0.1%
120.011 18
 
< 0.1%
119.994 17
 
< 0.1%
129.995 16
 
< 0.1%
119.969 16
 
< 0.1%
90.001 16
 
< 0.1%
Other values (84684) 170349
99.8%
ValueCountFrequency (%)
0 143
0.1%
30.946 1
 
< 0.1%
31.988 1
 
< 0.1%
32.466 1
 
< 0.1%
32.8 1
 
< 0.1%
32.941 1
 
< 0.1%
33.334 1
 
< 0.1%
33.391 1
 
< 0.1%
33.944 1
 
< 0.1%
34.496 1
 
< 0.1%
ValueCountFrequency (%)
243.507 1
< 0.1%
243.372 1
< 0.1%
238.895 1
< 0.1%
236.799 1
< 0.1%
224.437 1
< 0.1%
222.605 1
< 0.1%
221.741 1
< 0.1%
221.112 1
< 0.1%
221.058 2
< 0.1%
220.229 1
< 0.1%

speechiness
Real number (ℝ)

Distinct1626
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.098393262
Minimum0
Maximum0.97
Zeros143
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-01-20T12:40:11.872702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0281
Q10.0349
median0.045
Q30.0756
95-th percentile0.354
Maximum0.97
Range0.97
Interquartile range (IQR)0.0407

Descriptive statistics

Standard deviation0.16274007
Coefficient of variation (CV)1.6539758
Kurtosis17.000433
Mean0.098393262
Median Absolute Deviation (MAD)0.0131
Skewness4.0478485
Sum16791.105
Variance0.026484331
MonotonicityNot monotonic
2024-01-20T12:40:12.490747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0347 580
 
0.3%
0.0334 571
 
0.3%
0.0328 563
 
0.3%
0.0337 561
 
0.3%
0.033 559
 
0.3%
0.0332 557
 
0.3%
0.0349 557
 
0.3%
0.0363 553
 
0.3%
0.0362 553
 
0.3%
0.0344 552
 
0.3%
Other values (1616) 165047
96.7%
ValueCountFrequency (%)
0 143
0.1%
0.0222 1
 
< 0.1%
0.0223 3
 
< 0.1%
0.0224 5
 
< 0.1%
0.0225 4
 
< 0.1%
0.0226 5
 
< 0.1%
0.0227 7
 
< 0.1%
0.0228 9
 
< 0.1%
0.0229 7
 
< 0.1%
0.023 9
 
< 0.1%
ValueCountFrequency (%)
0.97 1
 
< 0.1%
0.969 3
 
< 0.1%
0.968 5
 
< 0.1%
0.967 12
 
< 0.1%
0.966 27
 
< 0.1%
0.965 34
 
< 0.1%
0.964 56
< 0.1%
0.963 73
< 0.1%
0.962 81
< 0.1%
0.961 106
0.1%

has_lyrics
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
120635 
0
50018 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters170653
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 120635
70.7%
0 50018
29.3%

Length

2024-01-20T12:40:13.030078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-20T12:40:13.499386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 120635
70.7%
0 50018
29.3%

Most occurring characters

ValueCountFrequency (%)
1 120635
70.7%
0 50018
29.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 170653
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 120635
70.7%
0 50018
29.3%

Most occurring scripts

ValueCountFrequency (%)
Common 170653
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 120635
70.7%
0 50018
29.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 170653
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 120635
70.7%
0 50018
29.3%

explicit
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
156220 
1
 
14433

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters170653
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 156220
91.5%
1 14433
 
8.5%

Length

2024-01-20T12:40:13.991581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-20T12:40:14.425857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 156220
91.5%
1 14433
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 156220
91.5%
1 14433
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 170653
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 156220
91.5%
1 14433
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 170653
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 156220
91.5%
1 14433
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 170653
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 156220
91.5%
1 14433
 
8.5%

valence
Real number (ℝ)

HIGH CORRELATION 

Distinct1733
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52858721
Minimum0
Maximum1
Zeros196
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-01-20T12:40:14.939074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0905
Q10.317
median0.54
Q30.747
95-th percentile0.937
Maximum1
Range1
Interquartile range (IQR)0.43

Descriptive statistics

Standard deviation0.26317146
Coefficient of variation (CV)0.49787709
Kurtosis-1.0616275
Mean0.52858721
Median Absolute Deviation (MAD)0.215
Skewness-0.10711976
Sum90204.993
Variance0.069259219
MonotonicityNot monotonic
2024-01-20T12:40:15.510849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 718
 
0.4%
0.962 593
 
0.3%
0.963 515
 
0.3%
0.964 462
 
0.3%
0.965 400
 
0.2%
0.96 388
 
0.2%
0.966 349
 
0.2%
0.967 316
 
0.2%
0.968 269
 
0.2%
0.559 248
 
0.1%
Other values (1723) 166395
97.5%
ValueCountFrequency (%)
0 196
0.1%
1 × 10-570
 
< 0.1%
6.41 × 10-51
 
< 0.1%
0.000537 1
 
< 0.1%
0.000562 1
 
< 0.1%
0.00126 1
 
< 0.1%
0.00166 1
 
< 0.1%
0.00173 1
 
< 0.1%
0.00213 1
 
< 0.1%
0.00228 1
 
< 0.1%
ValueCountFrequency (%)
1 4
< 0.1%
0.998 1
 
< 0.1%
0.996 2
 
< 0.1%
0.995 1
 
< 0.1%
0.994 3
 
< 0.1%
0.993 4
< 0.1%
0.991 4
< 0.1%
0.99 8
< 0.1%
0.989 6
< 0.1%
0.988 7
< 0.1%

voice_pitch
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1998441
Minimum0
Maximum11
Zeros21600
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-01-20T12:40:16.005898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5150939
Coefficient of variation (CV)0.67599986
Kurtosis-1.2710487
Mean5.1998441
Median Absolute Deviation (MAD)3
Skewness0.0058639886
Sum887369
Variance12.355885
MonotonicityNot monotonic
2024-01-20T12:40:16.414416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 21600
12.7%
7 20803
12.2%
2 18823
11.0%
9 17571
10.3%
5 16430
9.6%
4 12933
7.6%
1 12886
7.6%
10 12148
7.1%
8 10751
6.3%
11 10670
6.3%
Other values (2) 16038
9.4%
ValueCountFrequency (%)
0 21600
12.7%
1 12886
7.6%
2 18823
11.0%
3 7297
 
4.3%
4 12933
7.6%
5 16430
9.6%
6 8741
5.1%
7 20803
12.2%
8 10751
6.3%
9 17571
10.3%
ValueCountFrequency (%)
11 10670
6.3%
10 12148
7.1%
9 17571
10.3%
8 10751
6.3%
7 20803
12.2%
6 8741
5.1%
5 16430
9.6%
4 12933
7.6%
3 7297
 
4.3%
2 18823
11.0%

popularity
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.431794
Minimum0
Maximum100
Zeros27892
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-01-20T12:40:16.917130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median33
Q348
95-th percentile66
Maximum100
Range100
Interquartile range (IQR)37

Descriptive statistics

Standard deviation21.826615
Coefficient of variation (CV)0.694412
Kurtosis-1.025591
Mean31.431794
Median Absolute Deviation (MAD)17
Skewness-0.003733875
Sum5363930
Variance476.40113
MonotonicityNot monotonic
2024-01-20T12:40:17.510424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27892
 
16.3%
43 3136
 
1.8%
44 3117
 
1.8%
41 3078
 
1.8%
42 3051
 
1.8%
40 3051
 
1.8%
39 2968
 
1.7%
36 2896
 
1.7%
46 2883
 
1.7%
1 2876
 
1.7%
Other values (90) 115705
67.8%
ValueCountFrequency (%)
0 27892
16.3%
1 2876
 
1.7%
2 1733
 
1.0%
3 1467
 
0.9%
4 1114
 
0.7%
5 1018
 
0.6%
6 1017
 
0.6%
7 1116
 
0.7%
8 1128
 
0.7%
9 1213
 
0.7%
ValueCountFrequency (%)
100 1
 
< 0.1%
99 1
 
< 0.1%
97 1
 
< 0.1%
96 4
 
< 0.1%
95 4
 
< 0.1%
94 4
 
< 0.1%
93 4
 
< 0.1%
92 11
< 0.1%
91 9
< 0.1%
90 11
< 0.1%

Interactions

2024-01-20T12:39:52.399737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:46.318823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:51.781974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:57.341794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:02.938349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:08.745404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:14.216425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:19.851631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:25.299157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:30.772673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:36.166337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:41.583313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:46.993404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:52.822140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:46.771254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:52.194955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:57.783627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:03.359204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:09.194375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:14.646433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:20.254150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:25.721827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:31.193587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:36.578747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:42.007010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:47.413315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:53.244417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:47.195282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:52.606438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:58.225482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:03.780040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:09.629771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:15.078720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:20.683448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:26.147264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:31.605423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:36.999731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:42.426992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:47.833115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:53.666110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:47.611562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:53.028849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:58.674175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:04.571931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:10.039726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:15.509583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:21.100327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:26.528752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:32.037068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:37.404013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:42.843124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:48.243361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:54.598484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:48.021842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:53.451249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:59.133125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:04.976510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:10.469599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:15.943409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:21.521078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:26.959375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:32.460733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:37.819440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:43.253860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:48.663085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:55.011148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:48.445654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:53.862369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:59.609810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:05.387694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:10.879927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:16.393485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:21.939260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:27.401057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:32.862900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:38.261697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:43.666420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:49.079287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:55.433865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:48.877630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:54.295574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:00.047654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:05.819318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:11.320234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:16.833305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:22.379905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:27.836775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:33.294563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:38.684834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:44.105567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:49.503256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:55.834400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:49.291325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:54.719331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:00.464450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:06.220448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:11.733092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:17.253625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:22.813231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:28.260533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:33.698914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:39.104343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:44.524184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:49.933213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:56.254613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:49.715813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:55.142709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:00.875981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:06.632338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:12.154503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:17.696103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:23.213286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:28.670907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:34.131036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:39.526054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:44.947448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:50.344696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:56.657181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:50.136493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:55.535600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:01.290190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:07.054707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:12.544605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:18.125682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:23.643202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:29.092721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:34.533684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:39.920500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:45.348111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:50.756144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:57.072050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:50.553365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:55.983805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:01.702640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:07.455439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:12.965887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:18.546953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:24.054056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:29.518728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:34.947182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:40.337034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:45.757815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:51.173215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:57.494273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:50.954641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:56.444238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:02.114789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:07.909894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:13.375926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:18.976519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:24.466620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:29.935605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:35.350248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:40.740379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:46.161398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:51.587480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:57.905366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:51.369103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:38:56.906114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:02.526568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:08.310714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:13.796918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:19.426326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:24.890018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:30.354130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:35.753540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:41.169992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:46.572622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-20T12:39:51.989168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-20T12:40:17.939102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
acousticnessbpmdanceabilityduration_msenergyexplicithas_lyricsinstrumentalnesslivenessloudnesspopularityspeechinessvalencevoice_pitchyear
acousticness1.000-0.231-0.230-0.233-0.7590.2490.0610.2920.017-0.600-0.574-0.055-0.158-0.018-0.626
bpm-0.2311.0000.0220.0130.2630.0780.016-0.078-0.0050.2090.1380.0800.1720.0020.149
danceability-0.2300.0221.000-0.0880.2170.2830.062-0.281-0.1160.2330.1940.2390.5400.0230.195
duration_ms-0.2330.013-0.0881.0000.1770.0340.0160.106-0.0660.1250.231-0.094-0.199-0.0030.259
energy-0.7590.2630.2170.1771.0000.1550.048-0.2210.0670.8140.4870.1240.3590.0270.537
explicit0.2490.0780.2830.0340.1551.0000.079-0.2330.0540.1670.1910.343-0.0230.0050.231
has_lyrics0.0610.0160.0620.0160.0480.0791.000-0.0420.002-0.023-0.030-0.1040.015-0.115-0.035
instrumentalness0.292-0.078-0.2810.106-0.221-0.233-0.0421.000-0.053-0.339-0.297-0.101-0.167-0.012-0.288
liveness0.017-0.005-0.116-0.0660.0670.0540.002-0.0531.0000.030-0.1140.112-0.010-0.003-0.102
loudness-0.6000.2090.2330.1250.8140.167-0.023-0.3390.0301.0000.5050.0480.2630.0190.553
popularity-0.5740.1380.1940.2310.4870.191-0.030-0.297-0.1140.5051.000-0.0900.0060.0080.863
speechiness-0.0550.0800.239-0.0940.1240.343-0.104-0.1010.1120.048-0.0901.0000.1430.033-0.048
valence-0.1580.1720.540-0.1990.359-0.0230.015-0.167-0.0100.2630.0060.1431.0000.028-0.030
voice_pitch-0.0180.0020.023-0.0030.0270.005-0.115-0.012-0.0030.0190.0080.0330.0281.0000.008
year-0.6260.1490.1950.2590.5370.231-0.035-0.288-0.1020.5530.863-0.048-0.0300.0081.000

Missing values

2024-01-20T12:39:58.436513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-20T12:39:59.540143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

yearduration_msacousticnessdanceabilitylivenessloudnessenergyinstrumentalnessbpmspeechinesshas_lyricsexplicitvalencevoice_pitchpopularity
019218316670.9820.2790.665-20.0960.2110.87800080.9540.0366100.0594104
119211805330.7320.8190.160-12.4410.3410.00000060.9360.4150100.963075
219215000620.9610.3280.101-14.8500.1660.913000110.3390.0339100.039435
319212100000.9670.2750.381-9.3160.3090.000028100.1090.0354100.165053
419211666930.9570.4180.229-10.0960.1930.000002101.6650.0380100.253032
519213950760.5790.6970.130-12.5060.3460.168000119.8240.0700100.196026
619211595070.9960.5180.115-10.5890.2030.00000066.2210.0615100.406004
719212187730.9930.3890.363-21.0910.0880.52700092.8670.0456000.073112
819211615200.9960.4850.104-21.5080.1300.15100064.6780.0483000.721050
919211965600.9820.6840.504-16.4150.2570.000000109.3780.3990100.771080
yearduration_msacousticnessdanceabilitylivenessloudnessenergyinstrumentalnessbpmspeechinesshas_lyricsexplicitvalencevoice_pitchpopularity
17064320202283330.009520.9170.0774-10.4560.569000.000000144.0140.2790110.9070766
17064420202536130.310000.5620.1250-8.4800.686000.022500103.0540.0249100.4660766
17064520201905000.994000.2810.0995-31.4600.033300.95900090.2500.0348100.1690670
17064620202306000.204000.5980.1080-10.9910.472000.000015120.0800.2580110.5220066
17064720201335000.974000.1750.1130-35.0720.007590.92500070.8720.0454100.0838770
17064820203017140.084600.7860.0822-3.7020.808000.000289105.0290.0881100.6080772
17064920201506540.206000.7170.1010-6.0200.753000.000000137.9360.0605100.7340768
17065020202112800.101000.6340.2580-2.2260.858000.00000991.6880.0809000.6370476
17065120203371470.009980.6710.6430-7.1610.623000.00000875.0550.3080110.1950270
17065220201895070.132000.8560.1820-4.9280.721000.00471094.9910.1080110.6420774

Duplicate rows

Most frequently occurring

yearduration_msacousticnessdanceabilitylivenessloudnessenergyinstrumentalnessbpmspeechinesshas_lyricsexplicitvalencevoice_pitchpopularity# duplicates
68820201987200.2440.7300.1360-4.9360.7690.000021181.9880.1780100.594206
25419401954000.9910.5380.0935-9.4920.3890.299000104.5610.0334000.9041104
29519402104000.9680.5820.1180-6.6860.4810.06260078.2700.0345100.716704
68620201949330.4650.8020.1170-4.2940.8390.00005294.9970.0592000.861714
68920201987200.2440.7300.1360-4.9360.7690.000021181.9880.1780100.594214
15619301873730.9880.1710.3550-20.0700.2210.86800077.7680.0494000.534603
20219401759600.9680.3790.5760-5.5190.5570.00012984.1980.0499000.737103
21019401821330.9720.4060.3280-7.9480.8230.001420129.4760.0507000.674203
21219401833870.8890.5670.3330-7.2670.6850.50500092.6200.0622100.655203
22619401918400.9620.4660.1250-6.7280.5500.000451117.1010.0541100.712803